library_name, license, base_model, tags, datasets, model-index
| library_name | license | base_model | tags | datasets | model-index | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| transformers | apache-2.0 | Qwen/Qwen3-1.7B-Base |
|
|
|
See axolotl config
axolotl version: 0.10.0.dev0
base_model: Qwen/Qwen3-1.7B-Base
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rms_norm: true
liger_glu_activation: true
# torch_compile: true
strict: false
dataloader_prefetch_factor: 1
dataloader_num_workers: 2
dataloader_pin_memory: true
gc_steps: -1 # gc at the end of each epoch
chat_template: qwen3
datasets:
- path: winglian/OpenThoughts-114k-math-correct
type: chat_template
split: train
split_thinking: true
eot_tokens:
- "<|im_end|>"
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out-1.7b-sft
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project: kd-4b-math
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_torch_fused
adam_beta2: 0.999
lr_scheduler: rex
learning_rate: 3e-5
max_grad_norm: 0.2
save_safetensors: true
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
logging_steps: 1
flash_attention: true
warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 2
debug:
weight_decay: 0.0
special_tokens:
eos_token: <|im_end|>
deepspeed: deepspeed_configs/zero2_torch_compile.json
outputs/out-1.7b-sft
This model is a fine-tuned version of Qwen/Qwen3-1.7B-Base on the winglian/OpenThoughts-114k-math-correct dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.1
- Tokenizers 0.21.1